13A.1 Combining Sources of Predictive Skill to Support Effective Drought Early Warning (Invited Presentation)

Thursday, 16 January 2020: 10:30 AM
253C (Boston Convention and Exhibition Center)
Chris C. Funk, USGS EROS, Santa Barbara, CA; and G. Husak, A. McNally, K. R. Arsenault, and L. S. Harrison

Since 2015, the world has experienced numerous extreme weather and climate-related disasters, resulting in nearly a trillion dollars in damages. Floods, fires, and droughts have impacted millions of people, and the combination of climate change and increased human exposure ensures that these impacts see continued expansion. Within the universe of extreme weather, drought occupies a unique position. Drought’s multifaceted nature and slow onset can make it difficult to detect, but also relatively easy to predict, especially when multiple sources of predictive skill are combined effectively. The science of 21st century drought prediction can improve by understanding these sources of predictive skill, how climate change may be increasing the intensity of droughts, and how these sources of predictive skill can be connected in effective drought early warning systems. This talk covers these three topics, focusing on three sources of predictive power: climate, weather, and land state. These three components provide increasing levels of accuracy, but with decreasing levels of lead time. At the greatest lead time, the thermal inertia of the Earth’s oceans can provide the basis for reasonably accurate climate forecasts on time-scales of 1 to 8 months. At the one-to-four week time scale, weather and seasonal-to-subseasonal forecasts provide substantially more accurate information. Remote sensing products then provide information about precipitation, actual evapotranspiration, and vegetation health, at varying levels of resolution and lead time. Precipitation can be observed daily and tends to lead to soil moisture and vegetation deficits, but has a coarser resolution and lower accuracy than information provided by moderate or high-resolution remotely sensed imagery. This talk describes these sources of predictive skill, how they are being modulated by climate change, and how computing frameworks can be constructed to produce integrated drought forecasts. Examples are provided for sub-Saharan Africa.

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